A molecularly imprinted electrochemical sensor based on dual functional monomers for selective determination of nimodipine

Abstract

As a core medication for the prevention and treatment of cerebral vasospasm, especially after aneurysmal subarachnoid hemorrhage, real-time monitoring of nimodipine (NMD) concentration helps evaluate the adequacy of drug therapy and holds great significance for ensuring patient life and health. Herein, based on dual functional monomers, a molecularly imprinted electrochemical sensor (MIECS) for the detection of NMD with nitrogen-doped multi-walled carbon nanotubes (N-CNTs) and Fe-MOFs was developed. Fe-MOFs provided a large specific surface area, offering more space for generating imprinting sites, while N-CNTs enhanced the conductivity of the electrode. 2-Amino-5-mercapto-1,3,4-thiadiazole (AMT) and o-phenylenediamine (o-PD) served as dual functional monomers and NMD as the template molecule. A molecularly imprinted polymer (MIP) membrane was prepared on the electrode surface by electropolymerization. Compared to single functional monomers, the dual functional monomers exhibited better selectivity and specificity in NMD recognition. Under optimal experimental conditions, the response of the MIECS to NMD showed a linear relationship ranging from 10−14 M to 10−8 M, with a detection limit of 2.97 × 10−15 M. Satisfactory recovery rates were obtained in the detection of human serum and tablets. This multi-parametric enhancement establishes a new paradigm for therapeutic drug monitoring in clinical neurology and pharmaceutical quality control.

Graphical abstract: A molecularly imprinted electrochemical sensor based on dual functional monomers for selective determination of nimodipine

Supplementary files

Article information

Article type
Paper
Submitted
06 Sep 2025
Accepted
07 Jan 2026
First published
10 Feb 2026

Anal. Methods, 2026, Advance Article

A molecularly imprinted electrochemical sensor based on dual functional monomers for selective determination of nimodipine

Y. Wang, X. Sun, M. Liu, Z. Dai, X. Yang, L. Li and Y. Ding, Anal. Methods, 2026, Advance Article , DOI: 10.1039/D5AY01486G

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